An image processing method for the robust representation of objects is already known from the publication "Robust Object Representation Through Object-Relevant Use of a Scale" by Bryan Morse, Stephen Pizer et al. in "104, SPIE, Vol. 2167, Image Processing, 1994, pp. 104-115".
The cited publication describes an image segmentation method for separating the objects so as to present them in a form appropriate for follow-up (identification, tracking). The method aims to achieve such a representation by elimination of image deterioration due to noise, change in magnification without change of resolution, blurring etc.
The cited publication defines a first concept which is called "CORE" and is the location of points situated at the middle of the object as measured at scales proportional to the width of that object.
A point is said to be situated at the middle, or on a median line of the object, if it satisfies two conditions. The first condition is that there are necessarily at least two boundary points that lie at a distance r (referred to as half-width) or radius from this median point. The second condition is that the direction of said half-width r must be normal to said boundaries.
The cited publication defines a second concept for carrying out the method, being the scale of measurement which is defined by a parameter .sigma. which must be proportional to the half-width r.
The "CORE" concept in the cited publication does not represent objects at a single scale from one end of an image to the other, and even does not represent a given object at a single scale within the object itself. The CORE concept represents an object simultaneously in a range of scales forming separate curves within a scale space.
The steps of this segmentation method necessitate first of all the calculation of so-called CORE elements in sub-steps for:
1/smoothing the image at various scales in order to produce a scale space which describes the image over multiple resolutions, PA1 2/evaluating, at each scale, clouds of potential boundary points which form a first fuzzy assembly, called "boundariness", and are calculated by means of appropriate operators, PA1 3/evaluating clouds of median points which form a second fuzzy assembly, called "medialness", are defined by the association of all potential boundary points, and are calculated in the multi-resolution space in conformity with the two conditions described above, PA1 4/finding ridges in the so-called "medialness" fuzzy assembly, said ridges being intensity maxima of this second fuzzy assembly. PA1 1) forming smoothed images at several scales from the digital image, and for, in each smoothed image: PA1 2) extracting boundary pixels of objects, PA1 3) extracting potential median pixels associated with a location of a center of a circle having a radius which is linked to the scale by a proportionality constant k, tangent to boundaries at a pair of distinct boundary pixels, and associated with a measure of dependability that the center of the circle and the boundary pixels of the pair are substantially aligned, PA1 4) extracting median pixels by way of a first selection of potential median pixels extracted from different smoothed images which have the maximum measure of dependability for the same location, and by way of a second selection of remaining potential median pixels which locally have a maximum intensity substantially in the direction of alignment, and in the digital image formed by extracted median pixels: PA1 5) tracking extracted median pixels in order to construct skeletons of objects.
The method described in the cited document imposes the localization of the medial line or lines of an object in a digital image while utilizing clouds of boundary points leading to clouds of median points in order to preserve the fuzzy assembly notion as long as possible in a multi-resolution space, implying for each calculation two space variables which are the co-ordinates of the current point in the image, i.e. a variable .sigma. which is the core of the multi-resolution smoothing filters and is proportional to a radius r to be found, and an intensity variable which is related to said current point. These calculations must, therefore, be executed on an extremely large number of points, the whole operation having to be performed in a non-Euclidian geometry. This method is not specifically applied to perform the segmentation of an image representing objects of a predetermined specific shape.